Where \(L\)  is the maximum number of inputs masked, and \(c\)  is the output that the attribution is intended to explain (usually this is the highest output of the model, which corresponds to the class that the model assigned to \(\mathbf {x}\) ). For large images, we can approximate this value by taking
a fixed number of steps with a constant step size. The MoRF-variant evaluates the high end of the attribution map, whereas the LeRF-variant evaluates the low end. We choose \(L\)  such that at most 15% of pixels are masked, which corresponds to the original approach in [1]}. This limits the influence of out-of-distribution effects: as more pixels are removed, the image gets further removed from the original data manifold, making the result less representative. This metric scales linearly in the number of steps \(L\)  taken to compute the AUC.
